{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T23:03:26Z","timestamp":1789686206308,"version":"4.0.1"},"publisher-location":"Cham","reference-count":82,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032370310","type":"print"},{"value":"9783032370327","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-37032-7_27","type":"book-chapter","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T22:18:17Z","timestamp":1789683497000},"page":"494-514","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MobileSAM2: Lightweight Segment Anything for\u00a0Spatial Intelligence"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9921-2043","authenticated-orcid":false,"given":"Kai","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8681-0471","authenticated-orcid":false,"given":"Jiaxing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiying","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunsong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aoran","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dacheng","family":"Tao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"27_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107637","volume":"110","author":"S Bai","year":"2021","unstructured":"Bai, S., Zhang, F., Torr, P.H.: Hypergraph convolution and hypergraph attention. Pattern Recogn. 110, 107637 (2021)","journal-title":"Pattern Recogn."},{"key":"27_CR2","unstructured":"Black, K., et al.: $$pi_{0.5}$$: a vision-language-action model with open-world generalization. In: 9th Annual Conference on Robot Learning (2025)"},{"key":"27_CR3","unstructured":"Black, K., et al.: $$pi_{0}$$: A vision-language-action flow model for general robot control. arXiv preprint arXiv:2410.24164 (2024)"},{"key":"27_CR4","unstructured":"Carion, N., et al.: SAM 3: Segment anything with concepts (2025). https:\/\/arxiv.org\/abs\/2511.16719"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Ceylan, D., Huang, C.H.P., Mitra, N.J.: Pix2Video: video editing using image diffusion. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 23206\u201323217 (2023)","DOI":"10.1109\/ICCV51070.2023.02121"},{"key":"27_CR6","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J.P., Wang, C., Feng, Y., Chen, C.: Online knowledge distillation with diverse peers. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a034, pp. 3430\u20133437 (2020)","DOI":"10.1609\/aaai.v34i04.5746"},{"key":"27_CR7","doi-asserted-by":"crossref","unstructured":"Chen, D., Mei, J.P., Zhang, H., Wang, C., Feng, Y., Chen, C.: Knowledge distillation with the reused teacher classifier. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11933\u201311942 (2022)","DOI":"10.1109\/CVPR52688.2022.01163"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Chen, P., Liu, S., Zhao, H., Jia, J.: Distilling knowledge via knowledge review. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5008\u20135017 (2021)","DOI":"10.1109\/CVPR46437.2021.00497"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Deza, E., Deza, M.M., Deza, M.M., Deza, E.: Encyclopedia of distances. Springer (2009)","DOI":"10.1007\/978-3-642-00234-2"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Ding, H., Liu, C., He, S., Jiang, X., Torr, P.H., Bai, S.: MOSE: a new dataset for video object segmentation in complex scenes. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 20224\u201320234 (2023)","DOI":"10.1109\/ICCV51070.2023.01850"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Dupont, P.E., et al.: A decade retrospective of medical robotics research from 2010 to 2020. Sci. Robot. 6(60), eabi8017 (2021)","DOI":"10.1126\/scirobotics.abi8017"},{"issue":"1","key":"27_CR12","doi-asserted-by":"publisher","first-page":"45","DOI":"10.5198\/jtlu.2019.1405","volume":"12","author":"A Faisal","year":"2019","unstructured":"Faisal, A., Kamruzzaman, M., Yigitcanlar, T., Currie, G.: Understanding autonomous vehicles. J. Transp. Land Use 12(1), 45\u201372 (2019)","journal-title":"J. Transp. Land Use"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C.: X3D: expanding architectures for efficient video recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 203\u2013213 (2020)","DOI":"10.1109\/CVPR42600.2020.00028"},{"key":"27_CR14","unstructured":"Feng, Y., et al.: Hyper-YOLO: When visual object detection meets hypergraph computation. arXiv preprint arXiv:2408.04804 (2024)"},{"key":"27_CR15","unstructured":"Feng, Y., et al.: Hypergraph foundation model. arXiv preprint arXiv:2503.01203 (2025)"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3558\u20133565 (2019)","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"27_CR17","unstructured":"Fu, M., et al.: CaP-X: A framework for benchmarking and improving coding agents for robot manipulation. arXiv preprint arXiv:2603.22435 (2026)"},{"issue":"3","key":"27_CR18","doi-asserted-by":"publisher","first-page":"3181","DOI":"10.1109\/TPAMI.2022.3182052","volume":"45","author":"Y Gao","year":"2022","unstructured":"Gao, Y., Feng, Y., Ji, S., Ji, R.: HGNN+: general hypergraph neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 45(3), 3181\u20133199 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"5","key":"27_CR19","first-page":"2548","volume":"44","author":"Y Gao","year":"2020","unstructured":"Gao, Y., et al.: Hypergraph learning: methods and practices. IEEE Trans. Pattern Anal. Mach. Intell. 44(5), 2548\u20132566 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"6","key":"27_CR20","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vision 129(6), 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vision"},{"key":"27_CR21","doi-asserted-by":"crossref","unstructured":"Guo, Z., Yan, H., Li, H., Lin, X.: Class attention transfer based knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11868\u201311877 (2023)","DOI":"10.1109\/CVPR52729.2023.01142"},{"key":"27_CR22","doi-asserted-by":"crossref","unstructured":"Heo, B., Kim, J., Yun, S., Park, H., Kwak, N., Choi, J.Y.: A comprehensive overhaul of feature distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1921\u20131930 (2019)","DOI":"10.1109\/ICCV.2019.00201"},{"key":"27_CR23","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"27_CR24","doi-asserted-by":"crossref","unstructured":"Hong, L., et al.: LVOS: a benchmark for long-term video object segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 13480\u201313492 (2023)","DOI":"10.1109\/ICCV51070.2023.01240"},{"key":"27_CR25","doi-asserted-by":"crossref","unstructured":"Hou, Y., Ma, Z., Liu, C., Hui, T.W., Loy, C.C.: Inter-region affinity distillation for road marking segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12486\u201312495 (2020)","DOI":"10.1109\/CVPR42600.2020.01250"},{"key":"27_CR26","doi-asserted-by":"crossref","unstructured":"Huang, J., Zhang, J., Jiang, K., Lu, S.: Open-vocabulary object detection via language hierarchy. arXiv preprint arXiv:2410.20371 (2024)","DOI":"10.52202\/079017-3968"},{"key":"27_CR27","doi-asserted-by":"crossref","unstructured":"Javaid, M., Haleem, A., Singh, R.P., Suman, R.: Substantial capabilities of robotics in enhancing industry 4.0 implementation. Cogn. Robot. 1, 58\u201375 (2021)","DOI":"10.1016\/j.cogr.2021.06.001"},{"key":"27_CR28","doi-asserted-by":"crossref","unstructured":"Jiang, J., Wei, Y., Feng, Y., Cao, J., Gao, Y.: Dynamic hypergraph neural networks. In: IJCAI, pp. 2635\u20132641 (2019)","DOI":"10.24963\/ijcai.2019\/366"},{"issue":"11","key":"27_CR29","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011597","volume":"19","author":"S Jin","year":"2023","unstructured":"Jin, S., et al.: A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networks. PLoS Comput. Biol. 19(11), e1011597 (2023)","journal-title":"PLoS Comput. Biol."},{"key":"27_CR30","doi-asserted-by":"crossref","unstructured":"Jin, Y., Wang, J., Lin, D.: Multi-level logit distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24276\u201324285 (2023)","DOI":"10.1109\/CVPR52729.2023.02325"},{"key":"27_CR31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1016\/j.neucom.2023.02.006","volume":"531","author":"V Kamath","year":"2023","unstructured":"Kamath, V., Renuka, A.: Deep learning based object detection for resource constrained devices: Systematic review, future trends and challenges ahead. Neurocomputing 531, 34\u201360 (2023)","journal-title":"Neurocomputing"},{"key":"27_CR32","doi-asserted-by":"crossref","unstructured":"Ke, L., Ye, M., Danelljan, M., Tai, Y.W., Tang, C.K., Yu, F.: Segment anything in high quality. Adv. Neural. Inf. Process. Syst. 36 (2024)","DOI":"10.52202\/075280-1303"},{"key":"27_CR33","unstructured":"Kim, M.J., et al.: OpenVLA: An open-source vision-language-action model. arXiv preprint arXiv:2406.09246 (2024)"},{"key":"27_CR34","doi-asserted-by":"crossref","unstructured":"Kim, S., Lee, S.Y., Gao, Y., Antelmi, A., Polato, M., Shin, K.: A survey on hypergraph neural networks: an in-depth and step-by-step guide. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 6534\u20136544 (2024)","DOI":"10.1145\/3637528.3671457"},{"key":"27_CR35","unstructured":"Kirillov, A., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"27_CR36","doi-asserted-by":"publisher","first-page":"58603","DOI":"10.1109\/ACCESS.2022.3179047","volume":"10","author":"G Kornaros","year":"2022","unstructured":"Kornaros, G.: Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective. IEEE Access 10, 58603\u201358622 (2022)","journal-title":"IEEE Access"},{"key":"27_CR37","doi-asserted-by":"crossref","unstructured":"Lee, G., Bu, F., Eliassi-Rad, T., Shin, K.: A survey on hypergraph mining: Patterns, tools, and generators. arXiv preprint arXiv:2401.08878 (2024)","DOI":"10.1145\/3719002"},{"key":"27_CR38","unstructured":"Li, F., et al.: Semantic-SAM: Segment and recognize anything at any granularity. arXiv preprint arXiv:2307.04767 (2023)"},{"key":"27_CR39","doi-asserted-by":"crossref","unstructured":"Li, G., Li, X., Wang, Y., Zhang, S., Wu, Y., Liang, D.: Knowledge distillation for object detection via rank mimicking and prediction-guided feature imitation. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a036, pp. 1306\u20131313 (2022)","DOI":"10.1609\/aaai.v36i2.20018"},{"key":"27_CR40","doi-asserted-by":"crossref","unstructured":"Li, Z., Ye, J., Song, M., Huang, Y., Pan, Z.: Online knowledge distillation for efficient pose estimation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11740\u201311750 (2021)","DOI":"10.1109\/ICCV48922.2021.01153"},{"key":"27_CR41","doi-asserted-by":"crossref","unstructured":"Lin, S., et al.: Knowledge distillation via the target-aware transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10915\u201310924 (2022)","DOI":"10.1109\/CVPR52688.2022.01064"},{"key":"27_CR42","doi-asserted-by":"crossref","unstructured":"Liu, S., Zhang, Y., Li, W., Lin, Z., Jia, J.: Video-P2P: video editing with cross-attention control. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8599\u20138608 (2024)","DOI":"10.1109\/CVPR52733.2024.00821"},{"key":"27_CR43","doi-asserted-by":"crossref","unstructured":"Liu, Y., Chen, K., Liu, C., Qin, Z., Luo, Z., Wang, J.: Structured knowledge distillation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2604\u20132613 (2019)","DOI":"10.1109\/CVPR.2019.00271"},{"key":"27_CR44","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)"},{"key":"27_CR45","unstructured":"Mei, K., Delbracio, M., Talebi, H., Tu, Z., Patel, V.M., Milanfar, P.: Conditional diffusion distillation (2023)"},{"key":"27_CR46","doi-asserted-by":"crossref","unstructured":"Mirzadeh, S.I., Farajtabar, M., Li, A., Levine, N., Matsukawa, A., Ghasemzadeh, H.: Improved knowledge distillation via teacher assistant. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 5191\u20135198 (2020)","DOI":"10.1609\/aaai.v34i04.5963"},{"issue":"14","key":"27_CR47","doi-asserted-by":"publisher","first-page":"2162","DOI":"10.3390\/electronics11142162","volume":"11","author":"D Parekh","year":"2022","unstructured":"Parekh, D., et al.: A review on autonomous vehicles: progress, methods and challenges. Electronics 11(14), 2162 (2022)","journal-title":"Electronics"},{"key":"27_CR48","doi-asserted-by":"crossref","unstructured":"Park, W., Kim, D., Lu, Y., Cho, M.: Relational knowledge distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3967\u20133976 (2019)","DOI":"10.1109\/CVPR.2019.00409"},{"key":"27_CR49","doi-asserted-by":"crossref","unstructured":"Peng, B., et al.: Correlation congruence for knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5007\u20135016 (2019)","DOI":"10.1109\/ICCV.2019.00511"},{"key":"27_CR50","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"27_CR51","unstructured":"Ravi, N., et al.: SAM 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024). https:\/\/arxiv.org\/abs\/2408.00714"},{"key":"27_CR52","unstructured":"Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: FitNets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550 (2014)"},{"key":"27_CR53","unstructured":"Ryali, C., et\u00a0al.: Hiera: a hierarchical vision transformer without the bells-and-whistles. In: International Conference on Machine Learning. pp. 29441\u201329454. PMLR (2023)"},{"key":"27_CR54","unstructured":"Shu, H., et al.: TinySAM: Pushing the envelope for efficient segment anything model. arXiv preprint arXiv:2312.13789 (2023)"},{"issue":"1","key":"27_CR55","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1109\/JPROC.2022.3226481","volume":"111","author":"MMH Shuvo","year":"2022","unstructured":"Shuvo, M.M.H., Islam, S.K., Cheng, J., Morshed, B.I.: Efficient acceleration of deep learning inference on resource-constrained edge devices: a review. Proc. IEEE 111(1), 42\u201391 (2022)","journal-title":"Proc. IEEE"},{"key":"27_CR56","unstructured":"Tian, Y., Zhang, C., Guo, Z., Zhang, X., Chawla, N.: Learning MLPs on graphs: a unified view of effectiveness, robustness, and efficiency. In: The Eleventh International Conference on Learning Representations (2022)"},{"key":"27_CR57","doi-asserted-by":"crossref","unstructured":"Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. arXiv preprint arXiv:1906.05849 (2019)","DOI":"10.1007\/978-3-030-58621-8_45"},{"key":"27_CR58","doi-asserted-by":"crossref","unstructured":"Tung, F., Mori, G.: Similarity-preserving knowledge distillation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1365\u20131374 (2019)","DOI":"10.1109\/ICCV.2019.00145"},{"issue":"7","key":"27_CR59","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1038\/s42256-023-00684-8","volume":"5","author":"R Vi\u00f1as","year":"2023","unstructured":"Vi\u00f1as, R., et al.: Hypergraph factorization for multi-tissue gene expression imputation. Nature Mach. Intell. 5(7), 739\u2013753 (2023)","journal-title":"Nature Mach. Intell."},{"key":"27_CR60","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Luiten, J., Torr, P.H., Leibe, B.: Siam R-CNN: visual tracking by re-detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6578\u20136588 (2020)","DOI":"10.1109\/CVPR42600.2020.00661"},{"key":"27_CR61","unstructured":"Wang, Y., Kleinberg, J.: From graphs to hypergraphs: Hypergraph projection and its remediation. arXiv preprint arXiv:2401.08519 (2024)"},{"key":"27_CR62","doi-asserted-by":"crossref","unstructured":"Wu, K., et al.: TinyViT: fast pretraining distillation for small vision transformers. In: European Conference on Computer Vision, pp. 68\u201385. Springer (2022)","DOI":"10.1007\/978-3-031-19803-8_5"},{"issue":"5","key":"27_CR63","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.1109\/TMI.2019.2957097","volume":"39","author":"L Xiao","year":"2019","unstructured":"Xiao, L., et al.: Multi-hypergraph learning-based brain functional connectivity analysis in fMRI data. IEEE Trans. Med. Imaging 39(5), 1746\u20131758 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"27_CR64","doi-asserted-by":"crossref","unstructured":"Yang, A., Lin, S., Yeh, C.H., Shu, M., Yang, Y., Chang, X.: Context matters: Distilling knowledge graph for enhanced object detection. IEEE Transactions on Multimedia (2023)","DOI":"10.1109\/TMM.2023.3266897"},{"key":"27_CR65","doi-asserted-by":"crossref","unstructured":"Yang, C., Zhou, H., An, Z., Jiang, X., Xu, Y., Zhang, Q.: Cross-image relational knowledge distillation for semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12319\u201312328 (2022)","DOI":"10.1109\/CVPR52688.2022.01200"},{"issue":"4","key":"27_CR66","first-page":"1843","volume":"34","author":"D Yang","year":"2020","unstructured":"Yang, D., Qu, B., Yang, J., Cudr\u00e9-Mauroux, P.: LBSN2Vec++: heterogeneous hypergraph embedding for location-based social networks. IEEE Trans. Knowl. Data Eng. 34(4), 1843\u20131855 (2020)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"27_CR67","unstructured":"Yang, J., Martinez, B., Bulat, A., Tzimiropoulos, G.: Knowledge distillation via softmax regression representation learning. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"27_CR68","unstructured":"Yang, J., Gao, M., Li, Z., Gao, S., Wang, F., Zheng, F.: Track anything: Segment anything meets videos (2023)"},{"key":"27_CR69","first-page":"29918","volume":"38","author":"H Yao","year":"2026","unstructured":"Yao, H., et al.: Mulberry: Empowering MLLM with o1-like reasoning and reflection via collective monte Carlo tree search. Adv. Neural. Inf. Process. Syst. 38, 29918\u201329952 (2026)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR70","doi-asserted-by":"crossref","unstructured":"Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: fast optimization, network minimization and transfer learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4133\u20134141 (2017)","DOI":"10.1109\/CVPR.2017.754"},{"issue":"1","key":"27_CR71","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1038\/s42005-021-00637-w","volume":"4","author":"JG Young","year":"2021","unstructured":"Young, J.G., Petri, G., Peixoto, T.P.: Hypergraph reconstruction from network data. Commun. Phys. 4(1), 135 (2021)","journal-title":"Commun. Phys."},{"key":"27_CR72","doi-asserted-by":"crossref","unstructured":"Yuan, J., Phan, M.H., Liu, L., Liu, Y.: FAKD: feature augmented knowledge distillation for semantic segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 595\u2013605 (2024)","DOI":"10.1109\/WACV57701.2024.00065"},{"issue":"11","key":"27_CR73","doi-asserted-by":"publisher","first-page":"7040","DOI":"10.1109\/TITS.2020.3001154","volume":"22","author":"B Zhang","year":"2020","unstructured":"Zhang, B., Zhang, J.: A traffic surveillance system for obtaining comprehensive information of the passing vehicles based on instance segmentation. IEEE Trans. Intell. Transp. Syst. 22(11), 7040\u20137055 (2020)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"27_CR74","unstructured":"Zhang, C., et al.: Faster segment anything: Towards lightweight SAM for mobile applications. arXiv preprint arXiv:2306.14289 (2023)"},{"key":"27_CR75","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: R1-VL: learning to reason with multimodal large language models via step-wise group relative policy optimization. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1859\u20131869 (2025)","DOI":"10.1109\/ICCV51701.2025.00181"},{"key":"27_CR76","unstructured":"Zhang, R., et al.: Do not blindly imitate the teacher: Using perturbed loss for knowledge distillation. arXiv preprint arXiv:2305.05010 (2023)"},{"issue":"9","key":"27_CR77","doi-asserted-by":"publisher","first-page":"14148","DOI":"10.1109\/TITS.2022.3147770","volume":"23","author":"X Zhang","year":"2022","unstructured":"Zhang, X., Feng, Y., Angeloudis, P., Demiris, Y.: Monocular visual traffic surveillance: a review. IEEE Trans. Intell. Transp. Syst. 23(9), 14148\u201314165 (2022)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"27_CR78","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Xiang, T., Hospedales, T.M., Lu, H.: Deep mutual learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4320\u20134328 (2018)","DOI":"10.1109\/CVPR.2018.00454"},{"key":"27_CR79","unstructured":"Zhao, X., et al.: Fast segment anything. arXiv preprint arXiv:2306.12156 (2023)"},{"key":"27_CR80","unstructured":"Zhou, X., et al.: LIBERO-PRO: Towards robust and fair evaluation of vision-language-action models beyond memorization. arXiv preprint arXiv:2510.03827 (2025)"},{"key":"27_CR81","doi-asserted-by":"crossref","unstructured":"Zou, X., et al.: Segment everything everywhere all at once. arXiv preprint arXiv:2304.06718 (2023)","DOI":"10.52202\/075280-0868"},{"key":"27_CR82","doi-asserted-by":"crossref","unstructured":"Zu, C., et al.: Identifying high order brain connectome biomarkers via learning on hypergraph. In: Machine Learning in Medical Imaging: 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings 7, pp.\u00a01\u20139. Springer (2016)","DOI":"10.1007\/978-3-319-47157-0_1"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37032-7_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T22:18:37Z","timestamp":1789683517000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37032-7_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032370310","9783032370327"],"references-count":82,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37032-7_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"18 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}